Design method for fault-tolerant controller of quad-rotor unmanned aerial vehicle

Abstract

The invention discloses a fault-tolerant controller design method for a quad-rotor unmanned aerial vehicle, and relates to the technical field of quad-rotor unmanned aerial vehicle control, and the method comprises the steps: building a state-space equation; designing a state observer based on an interval type-2 fuzzy logic system, and calculating an observation error between an unmeasurable state and a state observation value; the attitude subsystem and the position subsystem of the four-rotor unmanned aerial vehicle are decomposed into two-order subsystems; aiming at the first-order subsystem, designing a virtual controller alpha i, 1; and aiming at a second-order subsystem, designing a preset time filter, a self-adaptive preset time fault-tolerant controller taui (t) triggered based on a mixed event, a parameter updating law # imgabs0 # and a parameter updating law # imgabs1 #. The fault-tolerant controller designed by the invention can ensure that the actual preset time of a closed-loop system of the four-rotor unmanned aerial vehicle is stable; and the minimum upper bound of the system stabilization time can be determined through a controller gain, so that the quad-rotor unmanned aerial vehicle can accurately track a reference trajectory under the conditions of only measurable output signals, saturated input and actuator faults.

Description

Technical Field

[0001] The present invention relates to the technical field of quad-rotor unmanned aerial vehicle control, and in particular to a fault-tolerant controller design method for a quad-rotor unmanned aerial vehicle. Background Art

[0002] Quadrotors have the advantages of vertical take-off and landing, fixed-point hovering, and high maneuverability, and have been rapidly developed and promoted in the military and civil fields. The trajectory tracking control of quadrotors has received extensive attention as a basic research work. However, quadrotors are inevitably affected by external interference in the actual flight environment, and their kinematic models have inherent nonlinear factors such as high complexity, strong coupling, and multiple uncertainties; in particular, factors such as actuator failure and saturated input also affect the stability of the closed-loop system of quadrotors, making the trajectory tracking control research of quadrotors challenging.

[0003] Most of the current fault-tolerant controller designs for quadrotor drones assume that the system state information is completely measurable. However, in the actual operating environment, it is difficult to accurately observe all the state information of the quadrotor drone, making it impossible for the above controller design scheme to achieve trajectory tracking control of the quadrotor drone with unmeasurable state. In addition, the onboard network communication resources of the quadrotor drone are subject to the hard constraints of its own structure. The traditional periodic sampling controller design scheme continuously occupies the communication bandwidth, causing mechanical wear of the actuator. Although the event-driven communication mechanism can alleviate the communication burden and reduce the transmission of redundant data, the control design method lacks flexibility. Summary of the invention

[0004] The present invention provides a fault-tolerant controller design method for a quadrotor drone, so as to achieve accurate observation of the unmeasurable system state of the quadrotor drone, and ensure that the quadrotor drone can quickly and accurately track the reference trajectory under the conditions of actuator failure, saturated input and limited network bandwidth.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The present invention provides a method for designing a fault-tolerant controller for a quad-rotor unmanned aerial vehicle, comprising:

[0007] A quadrotor UAV dynamics model with saturated input and actuator failure is constructed, and the state space equations are established accordingly;

[0008] A state observer based on interval type-2 fuzzy logic system is designed, and the unmeasurable state of the quadrotor drone is estimated by using the state observer to obtain the state observation value, and the observation error e between the unmeasurable state and the state observation value of the quadrotor drone is calculated. i ;

[0009] Based on the adaptive backstepping control method, the coordinate transformation equation is constructed in combination with the state observation value, and the attitude subsystem and position subsystem of the quadrotor UAV are decomposed into two-order subsystems.

[0010] Based on the coordinate transformation equation, a virtual controller α is designed for the first-order subsystem of the attitude subsystem and position subsystem of the quadrotor drone. i,1 ;

[0011] For the second-order subsystems of the attitude subsystem and position subsystem of the quadrotor drone, a scheduled time filter and an adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering are designed. i (t), parameter update law and parameter update law The closed-loop system of the attitude subsystem and position subsystem of the quadrotor drone is made to stabilize within a predetermined time, thereby obtaining the controller gain to be designed.

[0012] As a further description of the above technical solution, the dynamic model of a quadrotor drone with saturated input and actuator failure is:

[0013]

[0014] In the formula, φ, and are the angle, angular velocity and angular acceleration of the rolling angle of the quadrotor drone; θ, and are the pitch angle, angular velocity and angular acceleration of the quadrotor drone respectively; ψ, and are the angle, angular velocity and angular acceleration of the yaw angle of the quadrotor drone; z, and are the coordinates, speed and acceleration of the quadrotor drone in the z-axis direction; x, and are the coordinates, speed and acceleration of the quadrotor drone in the x-axis direction; y, and are the coordinates, velocity and acceleration of the quadrotor drone in the y-axis direction respectively; τ f , τ φ , τ θ and τ ψ are the total lift, roll angle control input, pitch angle control input and yaw angle control input of the quadrotor drone respectively; M is the mass of the quadrotor drone; is the distance from the center of mass of the quadrotor drone to the rotor; g is the acceleration due to gravity; J z , J x and J y are the rotational inertia in the z-axis, x-axis and y-axis directions respectively; and are sine function and cosine function respectively; C k is the drag coefficient; d k is an external unknown disturbance, satisfying is an unknown positive constant; k = φ, θ, ψ, z, x, y;

[0015] The state space equation is:

[0016]

[0017] Where: (x 1,1 ,x 2,1 ,x 3,1 ,x 4,1 ,x 5,1 ,x 6,1 )(φ,θ,ψ,z,x,y),

[0018] g4=g5=g6=1 / M,I1,I2,I3=(τ φ ,τ θ ,τ ψ ),

[0019]

[0020] (d1, d2, d3, d4, d5, d6) = d φ ,d θ ,d ψ ,d z ,d x ,d y ),

[0021] The saturation input constraints suffered by the quadrotor drone are:

[0022] sat(I i )=I i +Δ i

[0023] Where: and are the upper and lower bounds of the saturated input, respectively;

[0024] The actuator failure model suffered by the quadrotor drone is:

[0025] I i =ρ i τ i +Ψ i

[0026] In the formula, τi is the actual control input signal of the quadrotor drone, ρ i ∈(0,1] represents the actuator drive efficiency coefficient of the quadrotor drone, Ψ i is the unknown time-varying deviation fault parameter.

[0027] As a further description of the above technical solution, the method also includes using an interval type-2 fuzzy logic system Approximate nonlinear terms To satisfy:

[0028]

[0029] In the formula, i is the ideal weight vector, ω i is the approximate error, Indicates that the input is The basis functions of

[0030] Rewrite the state space equation as:

[0031]

[0032] In the formula, G i =B i g i , W i =B i ω i , D i =B i d i ,

[0033] Then the state observer based on interval type-2 fuzzy logic system is designed as:

[0034]

[0035] In the formula, and x i,1 、x i,2 and i The observed value of is the weight vector Ξ of the interval type-2 fuzzy logic system i The estimated value of k i,1 and k i,2 is a positive design parameter;

[0036] Calculate the observation error between the unmeasured state and the observed state of the quadrotor drone Further calculations yield:

[0037]

[0038] In the formula,

[0039] As a further description of the above technical solution, the coordinate transformation equation is:

[0040]

[0041] In the formula, and are the first error surface and the second error surface respectively, is the reference trajectory, is the output signal of the predetermined time filter, is an auxiliary variable.

[0042] As a further description of the above technical solution, the virtual controller α i,1 for:

[0043]

[0044] In the formula, r=δ / 2 1+η / 2 ,δ=36 η / 2 , 0<η<1, is a fractional-order operator, s, T r ,∈ i,1 and q i,1 is a positive design parameter, The reference trajectory The derivative of .

[0045] As a further description of the above technical solution, the predetermined time filter is:

[0046]

[0047] In the formula, is the filtering error, ∈ i,3 >0.

[0048] As a further description of the above technical solution, an adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering i (t) is:

[0049]

[0050] In the formula, κ i (t) = γ i (t)-τ * (t), 0<μ i (0)<1,μ i(t)∈(0,1), and i is a positive design parameter; j = 1,…,5;

[0051] Intermediate control signal γ i (t) is defined as:

[0052]

[0053] In the formula, σ i,1 and σ i,2 is a positive design parameter;

[0054] Virtual Controller α i,2 Designed for:

[0055]

[0056] In the formula, ∈ i,2 and q i,2 is a positive design parameter.

[0057] As a further description of the above technical solution, the parameter update law and parameter update law for:

[0058]

[0059] In the formula, λ=2+η,

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] (1) Different from the existing quadrotor UAV trajectory tracking controller design method which assumes that all system state information is fully measurable, the present invention designs an adaptive interval type-II fuzzy output feedback control strategy based on a state observer, which not only ensures that the quadrotor UAV accurately tracks the reference trajectory when only the output signal is measurable, but also removes the assumption that all system signals of the quadrotor UAV are always measurable.

[0062] (2) Different from the controller design method based on time sampling and the controller design method based on single-mode event triggering, the design method of the present invention proposes a hybrid event-triggered communication mechanism, which can adaptively switch the trigger protocol according to the system control input amplitude difference, which not only improves the communication resource utilization, but also avoids the mechanical wear of the actuator and the problem of excessive control input amplitude.

[0063] (3) The adaptive scheduled time fault-tolerant controller based on hybrid event triggering designed in the present invention makes the actual scheduled time of the quadrotor UAV closed-loop system stable, and the minimum upper bound of the stable time can be directly adjusted by a controller gain, and ensures the effectiveness and feasibility of the fault-tolerant controller design method provided by the present invention when the quadrotor UAV suffers from actuator failure, saturated input and bandwidth limitation.

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the embodiments of the present invention are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 It is a schematic diagram of a quadrotor drone;

[0067] Figure 2 It is a flow chart of a method for designing a fault-tolerant controller for a quad-rotor UAV according to the present invention;

[0068] Figure 3 It is a structural block diagram of the design of an adaptive scheduled time fault-tolerant controller for a quadrotor UAV based on hybrid event triggering;

[0069] Figure 4 It is the reference trajectory, actual trajectory and state observation trajectory diagram of the quadrotor drone in three-dimensional space;

[0070] Figure 5 It is the tracking error trajectory diagram of the attitude subsystem and position subsystem of the quadrotor drone;

[0071] Figure 6 It is the reference signal, output signal and observation signal curve diagram of the attitude subsystem and position subsystem of the quadrotor drone;

[0072] Figure 7 is the observation error diagram of the attitude subsystem and position subsystem of the quadrotor drone;

[0073] Figure 8 It is the control input curve diagram of the attitude subsystem and position subsystem of the quadrotor drone;

[0074] Fig. 9 It is the trigger interval diagram of the attitude subsystem and position subsystem of the quadrotor drone;

[0075] Fig.10 It is the adaptive parameter trajectory diagram of the attitude subsystem and position subsystem of the quadrotor drone. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0077] Please refer to Figure 1 , Figure 2 and Figure 3 The present invention provides a method for designing a fault-tolerant controller for a quad-rotor drone, comprising the following steps:

[0078] Step S1: According to Figure 1 The schematic diagram of the quadrotor UAV structure is shown below, and the following quadrotor UAV dynamics model with saturated input and actuator failure is constructed:

[0079]

[0080] In the formula, φ, and are the angle, angular velocity and angular acceleration of the rolling angle of the quadrotor drone; θ, and are the pitch angle, angular velocity and angular acceleration of the quadrotor drone respectively; ψ, and are the angle, angular velocity and angular acceleration of the yaw angle of the quadrotor drone; z, and are the coordinates, speed and acceleration of the quadrotor drone in the z-axis direction; x, and are the coordinates, speed and acceleration of the quadrotor drone in the x-axis direction; y, and are the coordinates, velocity and acceleration of the quadrotor drone in the y-axis direction respectively; τ f , τ φ , τ θ and τ ψ are the total lift, roll angle control input, pitch angle control input and yaw angle control input of the quadrotor drone respectively; M is the mass of the quadrotor drone; is the distance from the center of mass of the quadrotor drone to the rotor; g is the acceleration due to gravity; J z , J x and J y are the rotational inertia in the z-axis, x-axis and y-axis directions respectively; and are sine function and cosine function respectively; C k is the drag coefficient; d k is an external unknown disturbance, satisfying is an unknown positive constant; k = φ, θ, ψ, z, x, y.

[0081] The quadrotor UAV dynamics model (1) is transformed into the following state space equation:

[0082]

[0083] Where: (x 1,1 ,x 2,1 ,x 3,1 ,x 4,1 ,x 5,1 ,x 6,1 )=(φ,θ,ψ,z,x,y),

[0084] g4=g5=g6=1 / M,I1,I2,I3=(τ φ ,τ θ ,τ ψ ),

[0085]

[0086] (d1, d2, d3, d4, d5, d6) = d φ ,d θ ,d ψ ,d z ,d x ,d y ),

[0087] The saturation input constraints suffered by the quadrotor drone are:

[0088] sat(I i )=I i +Δ i (3) Where: and are the upper and lower bounds of the saturated input, respectively;

[0089] The actuator failure model suffered by the quadrotor drone is:

[0090] I i =ρ i τ i +Ψ i (4)

[0091] In the formula, τ iis the actual control input signal of the quadrotor drone, ρ i ∈(0,1] represents the actuator drive efficiency coefficient of the quadrotor drone, Ψ i is the unknown time-varying deviation fault parameter.

[0092] Step S2: for the state space equation (2) described in step S1, an interval type-2 fuzzy logic system is used. Approximate nonlinear terms To satisfy:

[0093]

[0094] In the formula, i is the ideal weight vector, ω i is the approximate error, Indicates that the input is The basis function of .

[0095] Furthermore, the state space equation (2) can be rewritten as:

[0096]

[0097] In the formula, G i =B i g i , W i =B i ω i , D i =B i d i ,

[0098] A state observer (7) based on interval type-2 fuzzy logic system is designed, and the state observer (7) is used to estimate the unmeasurable state of the quadrotor drone to obtain the state observation value.

[0099]

[0100] In the formula, as well as x i,1 、x i,2 and i The observed value of is the weight vector Ξ of the interval type-2 fuzzy logic system i The estimated value of k i,1 and k i,2 is a positive design parameter.

[0101] According to the state space equation (6) and the state observer (7), the observation error between the unmeasurable state of the quadrotor drone and the obtained state observation value is calculated: Further calculations yield:

[0102]

[0103] In the formula,

[0104] Step S3: Based on the adaptive backstepping control technology and in combination with the obtained state observation values, the attitude subsystem and position subsystem of the quadrotor drone are decomposed into two-order subsystems by constructing the coordinate transformation equation (9).

[0105]

[0106] In the formula, and are the first error surface and the second error surface respectively, is the reference trajectory, is the output signal of the predetermined time filter, is an auxiliary variable.

[0107] Step S4: Based on the coordinate transformation equation obtained in step S3, for the first-order subsystem of the attitude subsystem and the position subsystem of the quadrotor drone, construct the following equation containing the observation error e i The Lyapunov function V1:

[0108]

[0109] Where P i is a positive definite matrix, is the observation error e i The transpose of .

[0110] Furthermore, the time derivative of the Lyapunov function V1 is obtained. Using Young's inequality, we can calculate:

[0111]

[0112] In the formula, p i,1 =λ min (Q i )-3||P i || 2 , λ min (Q i ) is a positive definite matrix Q i The minimum eigenvalue of , for any given positive definite matrix Q i and the Hurwitz matrix A i , there is always Established.

[0113] Designing a virtual controller αi,1 for:

[0114]

[0115] In the formula, r=δ / 2 1+η / 2 ,δ=36 η / 2 , 0<η<1, is a fractional-order operator, s, T r , i,1 and q i,1 is a positive design parameter, The reference trajectory The derivative of .

[0116] The designed virtual controller α i,1 Substituting into equation (11), we can obtain by simplifying:

[0117]

[0118] In the formula, κ=0.2785.

[0119] Step S5: Based on formula (13), for the second-order subsystem of the attitude subsystem and position subsystem of the quadrotor drone, the Lyapunov function V2 is designed as:

[0120]

[0121] In the formula, and They are i and θ i Get estimated value.

[0122] Calculating the time derivative of the Lyapunov function V2 yields:

[0123]

[0124] Constructing auxiliary variables for:

[0125]

[0126] In the formula, is a positive design parameter.

[0127] The scheduled time filter is designed as:

[0128]

[0129] In the formula, is the filtering error, ∈ i,3 >0.

[0130] Considering the limited airborne network bandwidth resources of quadrotor drones, the traditional time trigger mechanism will inevitably occupy the communication bandwidth continuously, causing mechanical wear of the actuator. Therefore, in order to solve the problem of continuous occupation of communication bandwidth by the time trigger control strategy, the present invention designs the following adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering i (t):

[0131]

[0132] In the formula, κ i (t) = δ i (t)-τ i (t), 0<μ i (0)<1,μ i (t)∈(0,1), and i is a positive design parameter; j=1,…,5.

[0133] Furthermore, for the designed dynamic relative threshold trigger protocol and exponential decay trigger protocol (19), a corresponding adaptive scheduled time fault-tolerant controller based on hybrid event triggering is designed.

[0134] The dynamic relative threshold triggering protocol is case 1 in equation (19), that is:

[0135] |τ i (t)|<ξ i .

[0136] The exponential decay trigger protocol is case 2 in equation (19), that is:

[0137] |τ i (t)|≥ξ i .

[0138] Case 1: When |τ i (t)|<ξ i When the dynamic relative threshold trigger protocol is adopted to update the control input signal, the following intermediate control signal γ is designed. i (t):

[0139]

[0140] In the formula, σ i,1 and σ i,2 is a positive design parameter, the virtual controller α i,2 Designed for:

[0141]

[0142] In the formula, ∈ i,2 and q i,2 is a positive design parameter.

[0143] Furthermore, the adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering shown in formula (18) i (t) Redesigned as:

[0144]

[0145] In the formula, |Φ i,1 (t)|≤1,|Φ i,2 (t)|≤1.

[0146] Case 2: When |τ i (t)|≥ i When the exponential decay trigger protocol is adopted to avoid the problem of excessive control input amplitude, the intermediate control signal γ i (t) Designed to:

[0147]

[0148] In the formula,

[0149] Accordingly, the adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering shown in equation (18) is i (t) Redesigned as:

[0150]

[0151] In the formula, |Φ i,3 (t)|≤1.

[0152] Design the following parameter update law and parameter update law

[0153]

[0154] In the formula, λ=2+η,

[0155] definition The designed adaptive scheduled time fault-tolerant controller based on hybrid event triggering τ i (t), parameter update law And the parameter update law Substituting into formula (15), we can get:

[0156]

[0157] In the formula,

[0158]

[0159] The designed adaptive scheduled time fault-tolerant controller based on hybrid event triggering is proposed i (t), parameter update law And the parameter update law The closed-loop system of the attitude subsystem and position subsystem of the quadrotor drone can be made to stabilize within a predetermined time, thereby obtaining the controller gain to be designed.

[0160] Based on Lyapunov's predetermined time stability theory, the effectiveness of the invented fault-tolerant controller design method is demonstrated to ensure that the quadrotor UAV with unpredictable state can accurately track the reference trajectory under actuator failure and saturated input conditions.

[0161] Based on formula (27), we can calculate:

[0162]

[0163] The present invention discloses a method for designing a fault-tolerant controller for a quad-rotor unmanned aerial vehicle, comprising designing a state observer (7) based on an interval type II fuzzy logic system, a predetermined time filter (17), a virtual controller α i,1 、Virtual controller α i,2 , parameter update law Parameter update law and an adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering i (t), based on Lyapunov's predetermined time stability theory, the fault-tolerant controller of the quadrotor drone designed by the method of the present invention can ensure that the attitude subsystem and position subsystem of the quadrotor drone are actually stable at the predetermined time, and all signals of the closed-loop system will be stable at the predetermined time. The inner convergence is to the following interval:

[0164]

[0165] According to formula (17) and formula (18), Available in, is an unknown positive parameter. Given that κ i (t k,i )=0 and Then the system trigger time interval Therefore, the Zeno phenomenon will not occur.

[0166] The following simulation experiments are carried out in the simulation software MATLAB R2020a / SIMULINK to illustrate in detail the effectiveness and feasibility of the fault-tolerant controller design method proposed in the present invention.

[0167] The reference trajectory is set as:

[0168]

[0169] The external unknown disturbance settings are:

[0170]

[0171] The initial conditions of the quadrotor drone are selected as:

[0172] [φ(0),θ(0),ψ(0)]=[0,0,0], [z(0),x(0),y(0)]=[-0.2,1.1,-0.2], μ i (0) = 0.3,

[0173] The state observer gain of the interval model fuzzy logic system is selected as:

[0174]

[0175] The controller gain is selected as:

[0176] η=2 / 11,T r =3, s=0.8,

[0177]

[0178] σ １,j =σ 2,j =25, 3, j 10, σ 4,1 =σ 4,2 =σ 5,1 =σ 5,2 =σ 6,1 =σ 6,2 =1, ξ i =10,q i,1 =2,q i,2 =3.

[0179] The upper and lower bounds of the saturated input are set as:

[0180] The actuator fault parameters are selected as follows:

[0181]

[0182] The model parameters of the quadrotor UAV are provided in Table 1.

[0183] Table 1

[0184] The simulation results are plotted on Figures 4 to 10 . Figure 4 The reference trajectory, actual trajectory and state observer trajectory of the quadrotor drone in three-dimensional space are plotted respectively. Figure 5 The tracking error trajectory diagram of the attitude subsystem and position subsystem of the quadrotor drone is shown in Figure 2. From the simulation results, it is concluded that the tracking error is within the predetermined time. The inner region converges to a small area near the origin. Figure 6 The response curves of the reference trajectory, actual trajectory and observed trajectory of the attitude subsystem and position subsystem of the quadrotor drone are plotted respectively. Figure 7 It is the observation error trajectory diagram of the attitude subsystem and position subsystem of the quadrotor drone. From the simulation diagram, it can be concluded that the state observer based on the interval type-2 fuzzy logic system designed by the present invention can effectively estimate the unmeasurable system state of the quadrotor drone. Figure 8 are the control input signals of the attitude subsystem and position subsystem of the quadrotor UAV. The simulation results show that the control input signals of the controlled UAV are strictly constrained to the saturation area. Fig. 9 This is the trigger time interval diagram for the control input of the attitude subsystem and position subsystem of the quadrotor drone. The Zeno phenomenon is effectively avoided. Fig.10 The simulation results show that the fault-tolerant controller design method designed by the present invention makes the closed-loop system of the quadrotor UAV stable within the actual predetermined time, and ensures that the quadrotor UAV with actuator failure and saturated input can still accurately track the reference trajectory when only the output signal is observable.

[0185] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A fault-tolerant controller design method for a quadrotor drone. It is characterized in that include: A quadrotor UAV dynamics model with saturated input and actuator failure is constructed, and the state space equations are established accordingly; A state observer based on interval type-2 fuzzy logic system is designed, and the unmeasurable state of the quadrotor drone is estimated by using the state observer to obtain the state observation value, and the observation error e between the unmeasurable state and the state observation value of the quadrotor drone is calculated. i ; Based on the adaptive backstepping control method, the coordinate transformation equation is constructed in combination with the state observation value, and the attitude subsystem and position subsystem of the quadrotor UAV are decomposed into two-order subsystems. Based on the coordinate transformation equation, a virtual controller α is designed for the first-order subsystem of the attitude subsystem and position subsystem of the quadrotor drone. i,1 ; For the second-order subsystems of the attitude subsystem and position subsystem of the quadrotor drone, a scheduled time filter and an adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering are designed. i (t), parameter update law And parameter update law The closed-loop system of the attitude subsystem and position subsystem of the quadrotor drone is made to stabilize within a predetermined time, thereby obtaining the controller gain to be designed.

2. The fault-tolerant controller design method for a quadrotor drone according to claim 1, It is characterized in that The dynamic model of a quadrotor drone with saturated input and actuator failure is: In the formula, φ, and are the angle, angular velocity and angular acceleration of the rolling angle of the quadrotor drone; θ, and are the pitch angle, angular velocity and angular acceleration of the quadrotor drone respectively; ψ, and are the angle, angular velocity and angular acceleration of the yaw angle of the quadrotor drone; z, and are the coordinates, speed and acceleration of the quadrotor drone in the z-axis direction; x, and are the coordinates, speed and acceleration of the quadrotor drone in the x-axis direction; y, and are the coordinates, velocity and acceleration of the quadrotor drone in the y-axis direction respectively; τ f , τ φ , τ θ and τ ψ are the total lift, roll angle control input, pitch angle control input and yaw angle control input of the quadrotor drone respectively; M is the mass of the quadrotor drone; is the distance from the center of mass of the quadrotor drone to the rotor; g is the acceleration due to gravity; J z , J x and J y are the rotational inertia in the z-axis, x-axis and y-axis directions respectively; and are sine function and cosine function respectively; C k is the drag coefficient; d k is an external unknown disturbance, satisfying is an unknown positive constant; k = φ, θ, ψ, z, x, y; The state space equation is: For example:(x 1,1 , x 2,1 , x 3,1 , x 4,1 , x 5,1 , x 6,1 )=(ϕ,θ,ψ,z,x,y), g 4 =g 5 =g 6 =1 / M, I 1 ,I 2 ,I 3 =(t φ ,t θ ,t ψ ), (d 1 ,d 2 ,d 3 ,d 4 ,d 5 ,d 6 )=d φ ,d θ ,d ψ ,d z ,d x ,d y ), The saturation input constraints suffered by the quadrotor drone are: sat(I i )=I i +Δ i Where: and are the upper and lower bounds of the saturated input, respectively; The actuator failure model suffered by the quadrotor drone is: I i =ρ i t i +Ψ i In the formula, τ i is the actual control input signal of the quadrotor drone, ρ i ∈(0,1] represents the actuator drive efficiency coefficient of the quadrotor drone, Ψ i is the unknown time-varying deviation fault parameter.

3. The fault-tolerant controller design method for a quadrotor drone according to claim 2, It is characterized in that The method also includes using an interval type-2 fuzzy logic system Approximate nonlinear terms To satisfy: In the formula, i is the ideal weight vector, ω i is the approximate error, Indicates that the input is The basis functions of Rewrite the state space equation as: In the formula, W i =B i ω i , D i =B i d i , Then the state observer based on interval type-2 fuzzy logic system is designed as: In the formula, and x i,1 、x i,2 and i The observed value of is the weight vector Ξ of the interval type-2 fuzzy logic system i The estimated value of k i,1 and k i,2 is a positive design parameter; Calculate the observation error between the unmeasured state and the observed state of the quadrotor drone Further calculations yield: In the formula, 4. The fault-tolerant controller design method for a quadrotor drone according to claim 3, It is characterized in that The coordinate transformation equation is: In the formula, and are the first error surface and the second error surface respectively, is the reference trajectory, is the output signal of the predetermined time filter, is an auxiliary variable.

5. The fault-tolerant controller design method for a quadrotor drone according to claim 4, It is characterized in that Virtual Controller α i,1 for: In the formula, r=δ / 2 1+η / 2 ,δ=36 η / 2 , 0<1<, is a fractional-order operator, s, T r ,∈ i,1 and q i,1 is a positive design parameter, The reference trajectory The derivative of .

6. The fault-tolerant controller design method for a quadrotor drone according to claim 5, It is characterized in that The predetermined time filters are: In the formula, is the filtering error, ∈ i,3 >0.

7. The fault-tolerant controller design method for a quadrotor drone according to claim 6, It is characterized in that Adaptive scheduled time fault-tolerant controller τ based on hybrid event triggering i (t) is: In the formula, κ i (t) = γ i (t)-τ i (t), 0<μ i (0)<1,μ i (t)∈(0,1), and i is a positive design parameter; j = 1,…,5; Intermediate control signal γ i (t) is defined as: In the formula, σ i,1 and σ i,2 is a positive design parameter; Virtual Controller α i,2 Designed for: In the formula, i,2 and q i,2 is a positive design parameter.

8. The fault-tolerant controller design method for a quadrotor drone according to claim 7, It is characterized in that Parameter update law and parameter update law for: In the formula, λ=2+η,

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